@article {100430,
	title = {Exploring Equilibrium Points in a Long-term Glucose-insulin Model for Type I Diabetes: MPC Application in Automated Insulin Delivery Systems Using Functional Insulin Therapy Tools},
	journal = {International Journal Bioautomation},
	volume = {29},
	number = {1},
	year = {2025},
	pages = {51-76 },
	abstract = {
	This study explores a novel approach to regulate blood glucose levels in individuals with type I diabetes, employing the widely used model predictive control (MPC) strategy in type 1 diabetes mellitus therapy and clinical trials. The MPC algorithm is implemented based on Magdelaine{\textquoteright}s long-term glucose-insulin model, which encompasses real-life characteristics often absent in other prevalent models. The control strategy is evaluated through simulations involving 10 virtual patients from existing literature. The simulations encompass fasting scenarios and a closed-loop control scenario involving three meals. MPC results are compared to those of the {\textquotedblleft}optimal{\textquotedblright} conventional insulin daily injections therapy (open-loop treatment), especially under {\textquotedblleft}aggressive conditions{\textquotedblright} including elevated initial blood glucose levels, substantial carbohydrate intake, closely spaced meal times, and incorporating a time delay between plasma glucose concentration and its subcutaneous measurement. The MPC algorithm demonstrated remarkable efficacy in glycemic control for 80\% of patients, achieving an average time-in-range percentage exceeding 80\% with no hypoglycemic episodes. This aligns with the American Diabetes Association{\textquoteright}s recommendation of spending at least 70\% of the time in the target range for effective glycemic control and maintaining an average time spent in hypoglycemia of less than 4\%. However, the same MPC controller exhibited suboptimal performance for two patients, with an average time spent in hypoglycemia exceeding 8\%. These findings underscore the need for individualized adjustments of MPC parameters or alternative control strategies to optimize glycemic management in all patients.

},
	url = {http://www.biomed.bas.bg/bioautomation/2025/vol_29.1/files/29.1_04.pdf},
	author = {Hamata, Amor and Aissi, Salim}
}
